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Full-Text Articles in Physical Sciences and Mathematics
Uncovering Embarrassing Moments In In-Situ Exposure Of Incoming Mobile Messages, Chulhong Min, Saumay Pushp, Seungchul Lee, Inseok Hwang, Youngki Lee, Seungwoo Kang, Junehwa Song
Uncovering Embarrassing Moments In In-Situ Exposure Of Incoming Mobile Messages, Chulhong Min, Saumay Pushp, Seungchul Lee, Inseok Hwang, Youngki Lee, Seungwoo Kang, Junehwa Song
Research Collection School Of Computing and Information Systems
Mobile instant messengers serve as major interaction media for everyday chats. Contrary to the belief that a message is seen only by a designated receiver, it can be accidentally exposed to someone nearby and could result in embarrassing moments, for example, when the receiver is viewing pictures together with his friend upon the message arrival. To understand the significance of the problem and core factors that cause such embarrassments, we collected 961 in-situ responses from 14 participants upon the actual message arrival and analyzed them from the perspective of the receiver's situation. The results showed that 29% of message arrivals …
Towards Semantically Secure Outsourcing Of Association Rule Mining On Categorical Data, Junzuo Lai, Yingjiu Li, Robert H. Deng, Jian Weng, Chaowen Guan, Qiang Yan
Towards Semantically Secure Outsourcing Of Association Rule Mining On Categorical Data, Junzuo Lai, Yingjiu Li, Robert H. Deng, Jian Weng, Chaowen Guan, Qiang Yan
Research Collection School Of Computing and Information Systems
When outsourcing association rule mining to cloud, it is critical for data owners to protect both sensitive raw data and valuable mining results from being snooped at cloud servers. Previous solutions addressing this concern add random noise to the raw data and/or encrypt the raw data with a substitution mapping. However, these solutions do not provide semantic security; partial information about raw data or mining results can be potentially discovered by an adversary at cloud servers under a reasonable assumption that the adversary knows some plaintext–ciphertext pairs. In this paper, we propose the first semantically secure solution for outsourcing association …